SOURCE-LINKED INTELLIGENCE

Combining knowledge-guided process models and data-driven machine learning models for enhancing soil carbon modelling in space and time

CORDIS · observation · Publication date unknown

Combining knowledge-guided process models and data-driven machine learning models for enhancing soil carbon modelling in space and time Accurate modelling and mapping soil organic carbon (SOC) variations in space and time are crucial for supporting soil health restoration, guiding sustainable land management practices, and contributing to climate change mitigation strategies. Although data-driven approaches, such as machine learning (ML), has attracted many attention in soil mapping tasks for its powerful ability to learn from data, it is better at capturing soil spatial variation than soil temporal dynamics. By contrast, process-based models benefit from mechanistic knowledge to express physical, chemical and biological processes that govern SOC temporal changes. Therefore, integrating two types of models will promise means to represent physically plausible SOC dynamics while retaining the spatial prediction accuracy of ML models. However, a c

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

242260.56

unit

EUR